Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios.

ABSTRACT: Predicting runoff with precision holds immense importance for flood control, water resource management, and basin ecological dispatch. Deep learning, especially long short-term memory (LSTM) neural networks, has excelled in runoff prediction, often outperforming traditional hydrological models. Recent studies suggest that deep learning models employing the self-attention mechanism, such as Transformer and Informer, can achieve even better results than LSTM. However, research exploring the multi-step runoff prediction capabilities of these novel models across diverse scenarios remains scarce. In this investigation, we introduce a relative location coding-enhanced Informer model, termed Rel-Informer, and compare its performance in rainfall-runoff prediction against the standard Informer, Tra

SUBMITTER: He M 

PROVIDER: S-EPMC11829044 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature